Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations44993
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 MiB
Average record size in memory208.0 B

Variable types

Numeric8
Categorical18

Alerts

cb_person_cred_hist_length is highly overall correlated with person_age and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
loan_status is highly overall correlated with previous_loan_defaults_on_fileHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_emp_exp is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_home_ownership_MORTGAGE is highly overall correlated with person_home_ownership_RENTHigh correlation
person_home_ownership_RENT is highly overall correlated with person_home_ownership_MORTGAGEHigh correlation
previous_loan_defaults_on_file is highly overall correlated with loan_statusHigh correlation
person_education_Doctorate is highly imbalanced (89.5%)Imbalance
person_home_ownership_OTHER is highly imbalanced (97.4%)Imbalance
person_home_ownership_OWN is highly imbalanced (65.1%)Imbalance
loan_intent_HOMEIMPROVEMENT is highly imbalanced (51.1%)Imbalance
person_emp_exp has 9566 (21.3%) zerosZeros

Reproduction

Analysis started2025-10-28 00:01:13.368835
Analysis finished2025-10-28 00:01:29.344686
Duration15.98 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

person_age
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.748428
Minimum20
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:29.490283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q124
median26
Q330
95-th percentile39
Maximum94
Range74
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9097374
Coefficient of variation (CV)0.21297558
Kurtosis5.8496425
Mean27.748428
Median Absolute Deviation (MAD)3
Skewness1.9106032
Sum1248485
Variance34.924997
MonotonicityNot monotonic
2025-10-27T17:01:29.704737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235254
11.7%
245138
11.4%
254507
10.0%
224236
9.4%
263659
 
8.1%
273095
 
6.9%
282728
 
6.1%
292455
 
5.5%
302021
 
4.5%
311645
 
3.7%
Other values (46)10255
22.8%
ValueCountFrequency (%)
2017
 
< 0.1%
211289
 
2.9%
224236
9.4%
235254
11.7%
245138
11.4%
254507
10.0%
263659
8.1%
273095
6.9%
282728
6.1%
292455
5.5%
ValueCountFrequency (%)
941
 
< 0.1%
841
 
< 0.1%
801
 
< 0.1%
781
 
< 0.1%
761
 
< 0.1%
733
 
< 0.1%
707
< 0.1%
695
< 0.1%
671
 
< 0.1%
6611
< 0.1%

person_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
24836 
1
20157 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
024836
55.2%
120157
44.8%

Length

2025-10-27T17:01:29.898935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:30.055763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
024836
55.2%
120157
44.8%

Most occurring characters

ValueCountFrequency (%)
024836
55.2%
120157
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
024836
55.2%
120157
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
024836
55.2%
120157
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
024836
55.2%
120157
44.8%

person_income
Real number (ℝ)

Distinct33983
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79908.448
Minimum8000
Maximum2448661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:30.236149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile28364.6
Q147195
median67046
Q395778
95-th percentile166693.8
Maximum2448661
Range2440661
Interquartile range (IQR)48583

Descriptive statistics

Standard deviation63322.132
Coefficient of variation (CV)0.79243352
Kurtosis216.23575
Mean79908.448
Median Absolute Deviation (MAD)23117
Skewness9.6949923
Sum3.5953208 × 109
Variance4.0096924 × 109
MonotonicityNot monotonic
2025-10-27T17:01:30.433531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800015
 
< 0.1%
7301110
 
< 0.1%
369959
 
< 0.1%
609148
 
< 0.1%
370208
 
< 0.1%
609477
 
< 0.1%
730827
 
< 0.1%
490527
 
< 0.1%
369467
 
< 0.1%
608647
 
< 0.1%
Other values (33973)44908
99.8%
ValueCountFrequency (%)
800015
< 0.1%
80371
 
< 0.1%
81041
 
< 0.1%
81861
 
< 0.1%
82481
 
< 0.1%
82671
 
< 0.1%
82771
 
< 0.1%
83021
 
< 0.1%
85181
 
< 0.1%
93641
 
< 0.1%
ValueCountFrequency (%)
24486611
< 0.1%
22809801
< 0.1%
21391431
< 0.1%
20129541
< 0.1%
17412431
< 0.1%
17289741
< 0.1%
16615671
< 0.1%
16357571
< 0.1%
16219921
< 0.1%
14409821
< 0.1%

person_emp_exp
Real number (ℝ)

High correlation  Zeros 

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.394528
Minimum0
Maximum76
Zeros9566
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:30.613641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum76
Range76
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9271593
Coefficient of variation (CV)1.0987355
Kurtosis6.1148092
Mean5.394528
Median Absolute Deviation (MAD)3
Skewness1.9520452
Sum242716
Variance35.131217
MonotonicityNot monotonic
2025-10-27T17:01:30.878121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09566
21.3%
24134
9.2%
14061
9.0%
33890
8.6%
43524
 
7.8%
53000
 
6.7%
62717
 
6.0%
72204
 
4.9%
81890
 
4.2%
91575
 
3.5%
Other values (46)8432
18.7%
ValueCountFrequency (%)
09566
21.3%
14061
9.0%
24134
9.2%
33890
8.6%
43524
 
7.8%
53000
 
6.7%
62717
 
6.0%
72204
 
4.9%
81890
 
4.2%
91575
 
3.5%
ValueCountFrequency (%)
761
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
581
 
< 0.1%
571
 
< 0.1%
502
 
< 0.1%
492
 
< 0.1%
482
 
< 0.1%
475
< 0.1%
462
 
< 0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct4481
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.1768
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:31.283802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q15000
median8000
Q312237
95-th percentile24000
Maximum35000
Range34500
Interquartile range (IQR)7237

Descriptive statistics

Standard deviation6314.8027
Coefficient of variation (CV)0.6589467
Kurtosis1.3517206
Mean9583.1768
Median Absolute Deviation (MAD)3800
Skewness1.1797839
Sum4.3117587 × 108
Variance39876733
MonotonicityNot monotonic
2025-10-27T17:01:31.476380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100003617
 
8.0%
50002786
 
6.2%
60002425
 
5.4%
120002416
 
5.4%
150002004
 
4.5%
80001928
 
4.3%
40001406
 
3.1%
200001384
 
3.1%
30001378
 
3.1%
70001314
 
2.9%
Other values (4471)24335
54.1%
ValueCountFrequency (%)
5005
< 0.1%
5631
 
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
9002
 
< 0.1%
9121
 
< 0.1%
9221
 
< 0.1%
9501
 
< 0.1%
ValueCountFrequency (%)
35000234
0.5%
348261
 
< 0.1%
348001
 
< 0.1%
346641
 
< 0.1%
343751
 
< 0.1%
343221
 
< 0.1%
341211
 
< 0.1%
340004
 
< 0.1%
339502
 
< 0.1%
338001
 
< 0.1%

loan_int_rate
Real number (ℝ)

Distinct1302
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.006448
Minimum5.42
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:31.671481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.59
median11.01
Q312.99
95-th percentile16
Maximum20
Range14.58
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.9789854
Coefficient of variation (CV)0.27065821
Kurtosis-0.42051942
Mean11.006448
Median Absolute Deviation (MAD)2.13
Skewness0.21391504
Sum495213.11
Variance8.8743542
MonotonicityNot monotonic
2025-10-27T17:01:31.852090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.013328
 
7.4%
10.99804
 
1.8%
7.51798
 
1.8%
7.49687
 
1.5%
7.88673
 
1.5%
5.42608
 
1.4%
7.9606
 
1.3%
11.49514
 
1.1%
9.99484
 
1.1%
13.49475
 
1.1%
Other values (1292)36016
80.0%
ValueCountFrequency (%)
5.42608
1.4%
5.432
 
< 0.1%
5.442
 
< 0.1%
5.461
 
< 0.1%
5.475
 
< 0.1%
5.484
 
< 0.1%
5.494
 
< 0.1%
5.51
 
< 0.1%
5.513
 
< 0.1%
5.522
 
< 0.1%
ValueCountFrequency (%)
2084
0.2%
19.919
 
< 0.1%
19.91
 
< 0.1%
19.825
 
< 0.1%
19.81
 
< 0.1%
19.794
 
< 0.1%
19.744
 
< 0.1%
19.6912
 
< 0.1%
19.663
 
< 0.1%
19.621
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1397364
Minimum0
Maximum0.66
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:32.040190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.07
median0.12
Q30.19
95-th percentile0.31
Maximum0.66
Range0.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.087206717
Coefficient of variation (CV)0.62408016
Kurtosis1.0829359
Mean0.1397364
Median Absolute Deviation (MAD)0.05
Skewness1.0348421
Sum6287.16
Variance0.0076050114
MonotonicityNot monotonic
2025-10-27T17:01:32.232087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082593
 
5.8%
0.12421
 
5.4%
0.072415
 
5.4%
0.092295
 
5.1%
0.062242
 
5.0%
0.122216
 
4.9%
0.052176
 
4.8%
0.112158
 
4.8%
0.141960
 
4.4%
0.041950
 
4.3%
Other values (54)22567
50.2%
ValueCountFrequency (%)
024
 
0.1%
0.01315
 
0.7%
0.02942
 
2.1%
0.031488
3.3%
0.041950
4.3%
0.052176
4.8%
0.062242
5.0%
0.072415
5.4%
0.082593
5.8%
0.092295
5.1%
ValueCountFrequency (%)
0.661
 
< 0.1%
0.631
 
< 0.1%
0.622
 
< 0.1%
0.612
 
< 0.1%
0.591
 
< 0.1%
0.581
 
< 0.1%
0.571
 
< 0.1%
0.565
< 0.1%
0.555
< 0.1%
0.548
< 0.1%

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.866557
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:32.406510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.877167
Coefficient of variation (CV)0.66089309
Kurtosis3.7156528
Mean5.866557
Median Absolute Deviation (MAD)2
Skewness1.6293948
Sum263954
Variance15.032424
MonotonicityNot monotonic
2025-10-27T17:01:32.580107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
48652
19.2%
38310
18.5%
26536
14.5%
53082
 
6.8%
62966
 
6.6%
72889
 
6.4%
82800
 
6.2%
92685
 
6.0%
102457
 
5.5%
12715
 
1.6%
Other values (19)3901
8.7%
ValueCountFrequency (%)
26536
14.5%
38310
18.5%
48652
19.2%
53082
 
6.8%
62966
 
6.6%
72889
 
6.4%
82800
 
6.2%
92685
 
6.0%
102457
 
5.5%
11712
 
1.6%
ValueCountFrequency (%)
3023
0.1%
2915
< 0.1%
2829
0.1%
2723
0.1%
2620
< 0.1%
2522
< 0.1%
2433
0.1%
2326
0.1%
2231
0.1%
2124
0.1%

credit_score
Real number (ℝ)

Distinct335
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.58571
Minimum390
Maximum784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2025-10-27T17:01:32.764241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile539
Q1601
median640
Q3670
95-th percentile703
Maximum784
Range394
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.402411
Coefficient of variation (CV)0.079676808
Kurtosis0.19244114
Mean632.58571
Median Absolute Deviation (MAD)33
Skewness-0.61532878
Sum28461929
Variance2540.403
MonotonicityNot monotonic
2025-10-27T17:01:32.955281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658406
 
0.9%
649398
 
0.9%
652396
 
0.9%
663394
 
0.9%
647393
 
0.9%
654391
 
0.9%
650391
 
0.9%
653390
 
0.9%
667390
 
0.9%
656386
 
0.9%
Other values (325)41058
91.3%
ValueCountFrequency (%)
3901
 
< 0.1%
4181
 
< 0.1%
4191
 
< 0.1%
4201
 
< 0.1%
4211
 
< 0.1%
4301
 
< 0.1%
4312
< 0.1%
4341
 
< 0.1%
4354
< 0.1%
4372
< 0.1%
ValueCountFrequency (%)
7842
< 0.1%
7731
< 0.1%
7721
< 0.1%
7701
< 0.1%
7681
< 0.1%
7671
< 0.1%
7651
< 0.1%
7642
< 0.1%
7621
< 0.1%
7602
< 0.1%

previous_loan_defaults_on_file
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
1
22856 
0
22137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
122856
50.8%
022137
49.2%

Length

2025-10-27T17:01:33.149190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:33.287852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
122856
50.8%
022137
49.2%

Most occurring characters

ValueCountFrequency (%)
122856
50.8%
022137
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
122856
50.8%
022137
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
122856
50.8%
022137
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
122856
50.8%
022137
49.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
32967 
1
12026 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%

Length

2025-10-27T17:01:33.438344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:33.573644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%

Most occurring characters

ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
032967
73.3%
112026
 
26.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
31597 
1
13396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
031597
70.2%
113396
29.8%

Length

2025-10-27T17:01:33.727403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:33.862036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
031597
70.2%
113396
29.8%

Most occurring characters

ValueCountFrequency (%)
031597
70.2%
113396
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
031597
70.2%
113396
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
031597
70.2%
113396
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
031597
70.2%
113396
29.8%

person_education_Doctorate
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
44372 
1
 
621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%

Length

2025-10-27T17:01:34.028330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:34.199421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%

Most occurring characters

ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
044372
98.6%
1621
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
33023 
1
11970 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%

Length

2025-10-27T17:01:34.350995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:34.487180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%

Most occurring characters

ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
033023
73.4%
111970
 
26.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
38013 
1
6980 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

Length

2025-10-27T17:01:34.641369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:34.778063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

Most occurring characters

ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
038013
84.5%
16980
 
15.5%

person_home_ownership_MORTGAGE
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
26508 
1
18485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026508
58.9%
118485
41.1%

Length

2025-10-27T17:01:34.923666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:35.233759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
026508
58.9%
118485
41.1%

Most occurring characters

ValueCountFrequency (%)
026508
58.9%
118485
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
026508
58.9%
118485
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
026508
58.9%
118485
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
026508
58.9%
118485
41.1%

person_home_ownership_OTHER
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
44876 
1
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

Length

2025-10-27T17:01:35.384166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:35.541813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

Most occurring characters

ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
044876
99.7%
1117
 
0.3%

person_home_ownership_OWN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
42042 
1
 
2951

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

Length

2025-10-27T17:01:35.739237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:35.878415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

Most occurring characters

ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
042042
93.4%
12951
 
6.6%

person_home_ownership_RENT
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
1
23440 
0
21553 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
123440
52.1%
021553
47.9%

Length

2025-10-27T17:01:36.023945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:36.171784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
123440
52.1%
021553
47.9%

Most occurring characters

ValueCountFrequency (%)
123440
52.1%
021553
47.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
123440
52.1%
021553
47.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
123440
52.1%
021553
47.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
123440
52.1%
021553
47.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
37848 
1
7145 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%

Length

2025-10-27T17:01:36.336818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:36.477495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%

Most occurring characters

ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
037848
84.1%
17145
 
15.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
35842 
1
9151 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

Length

2025-10-27T17:01:36.625983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:36.761520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

Most occurring characters

ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
035842
79.7%
19151
 
20.3%

loan_intent_HOMEIMPROVEMENT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
40210 
1
4783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%

Length

2025-10-27T17:01:36.916728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:37.058419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%

Most occurring characters

ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
040210
89.4%
14783
 
10.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
36445 
1
8548 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%

Length

2025-10-27T17:01:37.205174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:37.347982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%

Most occurring characters

ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
036445
81.0%
18548
 
19.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
37442 
1
7551 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%

Length

2025-10-27T17:01:37.493223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:37.636741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%

Most occurring characters

ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
037442
83.2%
17551
 
16.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
37178 
1
7815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

Length

2025-10-27T17:01:37.782351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:37.919741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

Most occurring characters

ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
037178
82.6%
17815
 
17.4%

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
0
34993 
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Length

2025-10-27T17:01:38.073615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-27T17:01:38.221025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Most occurring characters

ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034993
77.8%
110000
 
22.2%

Interactions

2025-10-27T17:01:26.668143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:17.382377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-10-27T17:01:20.038941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.413495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:22.906254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.146635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.406309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.828366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:17.601760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:18.903534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:20.207488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.582695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.057579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.313552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.566051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.985567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:17.809623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.047663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:20.368364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.732306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.199223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.460385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.717437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:27.163198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:17.958511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.211266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:20.538854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.938946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.390515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.621619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.881726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:27.513547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:18.115843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.370108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:20.719049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:22.098239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.548431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.783768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.032625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:27.678855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:18.267456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.541907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:20.891518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:22.428818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.692336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:24.941412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.186067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:27.836248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:18.417608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.714341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.053052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:22.584415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.838053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.092651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.342433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:27.987017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:18.563302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:19.863311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:21.240042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:22.738408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:23.984392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:25.247116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-27T17:01:26.491775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-10-27T17:01:38.359115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
cb_person_cred_hist_lengthcredit_scoreloan_amntloan_int_rateloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTUREloan_percent_incomeloan_statusperson_ageperson_education_Associateperson_education_Bachelorperson_education_Doctorateperson_education_High Schoolperson_education_Masterperson_emp_expperson_genderperson_home_ownership_MORTGAGEperson_home_ownership_OTHERperson_home_ownership_OWNperson_home_ownership_RENTperson_incomeprevious_loan_defaults_on_file
cb_person_cred_hist_length1.0000.1420.0430.0170.0030.0860.0760.0230.0580.012-0.0370.0200.8210.0470.0990.1180.1070.0560.7510.0260.0460.0110.0080.0430.0930.026
credit_score0.1421.0000.0070.0110.0120.0230.0080.0000.0000.007-0.0120.0000.1600.0560.0780.1470.1720.1490.1710.0080.0000.0090.0000.0000.0220.179
loan_amnt0.0430.0071.0000.1050.0150.0270.0500.0360.0170.0190.6660.1260.0640.0000.0000.0000.0110.0040.0520.0050.1510.0220.0390.1380.4050.067
loan_int_rate0.0170.0110.1051.0000.0040.0250.0310.0060.0000.0140.1240.3630.0130.0060.0030.0080.0040.0000.0160.0000.1360.0240.0160.142-0.0330.198
loan_intent_DEBTCONSOLIDATION0.0030.0120.0150.0041.0000.2190.1500.2100.1950.1990.0200.0840.0000.0000.0080.0000.0040.0000.0000.0000.0150.0000.0940.0310.0090.045
loan_intent_EDUCATION0.0860.0230.0270.0250.2191.0000.1740.2450.2270.2320.0160.0640.0810.0000.0000.0140.0130.0060.0600.0000.0000.0030.0000.0000.0130.038
loan_intent_HOMEIMPROVEMENT0.0760.0080.0500.0310.1500.1741.0000.1670.1550.1580.0230.0330.0740.0150.0100.0090.0110.0040.0510.0000.0500.0000.0090.0550.0000.021
loan_intent_MEDICAL0.0230.0000.0360.0060.2100.2450.1671.0000.2170.2220.0230.0650.0310.0030.0000.0060.0000.0070.0320.0040.0540.0000.0150.0610.0110.034
loan_intent_PERSONAL0.0580.0000.0170.0000.1950.2270.1550.2171.0000.2060.0080.0220.0570.0000.0000.0000.0000.0020.0540.0000.0110.0000.0010.0140.0180.000
loan_intent_VENTURE0.0120.0070.0190.0140.1990.2320.1580.2220.2061.0000.0110.0860.0150.0010.0000.0000.0000.0000.0120.0000.0070.0050.0910.0370.0140.052
loan_percent_income-0.037-0.0120.6660.1240.0200.0160.0230.0230.0080.0111.0000.415-0.0560.0000.0000.0000.0000.006-0.0500.0000.1550.0120.0550.128-0.3530.220
loan_status0.0200.0000.1260.3630.0840.0640.0330.0650.0220.0860.4151.0000.0200.0000.0000.0000.0000.0000.0150.0000.2130.0120.0930.2550.0240.543
person_age0.8210.1600.0640.0130.0000.0810.0740.0310.0570.015-0.0560.0201.0000.0460.0980.1140.0970.0510.8870.0270.0490.0110.0090.0460.1430.028
person_education_Associate0.0470.0560.0000.0060.0000.0000.0150.0030.0000.0010.0000.0000.0461.0000.3930.0710.3640.2590.0410.0000.0000.0000.0000.0000.0050.010
person_education_Bachelor0.0990.0780.0000.0030.0080.0000.0100.0000.0000.0000.0000.0000.0980.3931.0000.0770.3920.2790.0810.0000.0040.0030.0000.0060.0000.017
person_education_Doctorate0.1180.1470.0000.0080.0000.0140.0090.0060.0000.0000.0000.0000.1140.0710.0771.0000.0710.0500.1070.0000.0080.0000.0000.0090.0200.019
person_education_High School0.1070.1720.0110.0040.0040.0130.0110.0000.0000.0000.0000.0000.0970.3640.3920.0711.0000.2580.0670.0000.0000.0000.0030.0000.0180.029
person_education_Master0.0560.1490.0040.0000.0000.0060.0040.0070.0020.0000.0060.0000.0510.2590.2790.0500.2581.0000.0400.0000.0000.0060.0070.0000.0000.021
person_emp_exp0.7510.1710.0520.0160.0000.0600.0510.0320.0540.012-0.0500.0150.8870.0410.0810.1070.0670.0401.0000.0260.0330.0110.0090.0350.1200.031
person_gender0.0260.0080.0050.0000.0000.0000.0000.0040.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0261.0000.0000.0000.0000.0000.0040.000
person_home_ownership_MORTGAGE0.0460.0000.1510.1360.0150.0000.0500.0540.0110.0070.1550.2130.0490.0000.0040.0080.0000.0000.0330.0001.0000.0420.2210.8710.0850.115
person_home_ownership_OTHER0.0110.0090.0220.0240.0000.0030.0000.0000.0000.0050.0120.0120.0110.0000.0030.0000.0000.0060.0110.0000.0421.0000.0120.0530.0180.009
person_home_ownership_OWN0.0080.0000.0390.0160.0940.0000.0090.0150.0010.0910.0550.0930.0090.0000.0000.0000.0030.0070.0090.0000.2210.0121.0000.2760.0000.053
person_home_ownership_RENT0.0430.0000.1380.1420.0310.0000.0550.0610.0140.0370.1280.2550.0460.0000.0060.0090.0000.0000.0350.0000.8710.0530.2761.0000.0820.138
person_income0.0930.0220.405-0.0330.0090.0130.0000.0110.0180.014-0.3530.0240.1430.0050.0000.0200.0180.0000.1200.0040.0850.0180.0000.0821.0000.019
previous_loan_defaults_on_file0.0260.1790.0670.1980.0450.0380.0210.0340.0000.0520.2200.5430.0280.0100.0170.0190.0290.0210.0310.0000.1150.0090.0530.1380.0191.000

Missing values

2025-10-27T17:01:28.243064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-27T17:01:28.905801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

person_ageperson_genderperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileperson_education_Associateperson_education_Bachelorperson_education_Doctorateperson_education_High Schoolperson_education_Masterperson_home_ownership_MORTGAGEperson_home_ownership_OTHERperson_home_ownership_OWNperson_home_ownership_RENTloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTUREloan_status
022.0171948.0035000.016.020.493.056100000100010000101
121.0112282.001000.011.140.082.050410001000100100000
225.0112438.035500.012.870.443.063500001010000001001
323.0179753.0035000.015.230.442.067500100000010001001
424.0066135.0135000.014.270.534.058600000100010001001
521.0112951.002500.07.140.192.053200001000100000011
626.0193471.0135000.012.420.373.070100100000010100001
724.0195550.0535000.011.110.374.058500001000010001001
824.01100684.0335000.08.900.352.054401000000010000101
921.0112739.001600.014.740.133.064000001000100000011
person_ageperson_genderperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileperson_education_Associateperson_education_Bachelorperson_education_Doctorateperson_education_High Schoolperson_education_Masterperson_home_ownership_MORTGAGEperson_home_ownership_OTHERperson_home_ownership_OWNperson_home_ownership_RENTloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTUREloan_status
4498331.00136832.0912319.016.920.097.072200000100010000101
4498424.0037786.0013500.013.430.364.061200001010000100001
4498523.0140925.009000.011.010.224.048700100000010000101
4498627.0135512.045000.015.830.145.050500001000010000101
4498724.0131924.0212229.010.700.384.067801000000010001001
4498827.0047971.0615000.015.660.313.064501000000010001001
4498937.0165800.0179000.014.070.1411.062101000000010010001
4499033.0056942.072771.010.020.0510.066801000000011000001
4499129.0033164.0412000.013.230.366.060400100000010100001
4499224.0051609.016665.017.050.133.062800001000011000001